Contrastive meta-reinforcement learning for heterogeneous graph neural architecture search

Abstract

Heterogeneous Graph Neural Networks (HGNNs) have demonstrated significant success in capturing complex interactions within heterogeneous graphs to learn graph representations. However, designing effective HGNN architecture is a challenging task that demands substantial domain knowledge and human effort. Fortunately, the advent of Heterogeneous Graph Architecture Search (HGNAS) has automated this process and yielded networks surpassing those designed by humans. Nonetheless, conventional HGNAS approaches are limited in their ability to generalize to unseen or diverse task types. To address these limitations, we propose an efficient method called Contrastive Meta-reinforcement learning-based Heterogeneous Graph Neural Architecture Search (CM-HGNAS). Our approach aims to produce high-performance networks tailored to different types of tasks. Firstly, we use gradient-based meta-learning to rapidly adapt to new tasks by leveraging the knowledge acquired from meta-training tasks. Secondly, since our meta-training tasks encompass various types of tasks, including node classification and link prediction, we employ contrastive learning to unify the evaluation metric across all tasks. This unification enhances the generalization capabilities of our model across diverse tasks. Experimental results demonstrate that our method successfully generalizes to unseen tasks and surpasses existing HGNN baselines and HGNAS techniques in terms of performance.